Product Engineer (AI Infrastructure)

Location: London, UK. Flexible working arrangements are available remotely or in a hybrid format, depending on the role and location.

Company

hirify.global operates Hyperstack, a self-serve GPU cloud and AI infrastructure platform for high-performance AI, HPC, and compute-heavy workloads, powered by renewable energy and NVIDIA architecture.

What you will do

• Own user-facing products end to end, including web applications, APIs, backend services, and CLI tools, primarily in Go.
• Build and maintain Kubernetes-based services, controllers, and platform capabilities, including GPU scheduling and container runtimes.
• Turn customer problems into practical solutions with Product, Engineering, DevOps, Infrastructure, Data Centre Operations, Security, and Customer Support.
• Automate repetitive operational work and investigate production issues across application code, Kubernetes, containers, Linux, networking, drivers, and hardware.
• Design services that perform reliably under load and failure, with observability, secure defaults, guardrails, and durable fixes.
• Contribute to product decisions about whether workflows are best delivered through a CLI, API, or web experience.

Requirements

• Strong professional experience building, debugging, and operating production software in Go; deep experience in another systems language is acceptable if rapid productivity in Go can be demonstrated.
• Hands-on Kubernetes and container experience, including workload scheduling, service communication, and troubleshooting misbehaving workloads.
• Confidence in Linux environments, including systemd, processes, signals, filesystems, permissions, networking, and CPU and memory diagnostics.
• A track record of personally designing and building systems, products, or tools.
• Solid engineering fundamentals in concurrency, API and interface design, failure modes, and production troubleshooting with logs, metrics, and system-level tooling.

Nice to have

• Familiarity with the NVIDIA GPU ecosystem, including drivers, CUDA, nvidia-smi, GPU-enabled container runtimes, or Kubernetes GPU scheduling.
• Experience with distributed and event-driven systems, including queues, retries, idempotency, and backpressure.
• Practical networking knowledge covering DNS, TCP/IP, HTTP, and load balancing.
• Experience building Kubernetes controllers or operators, or developer tooling used by other engineers.
• A security-minded approach to authentication, secrets, and attack surfaces.

Culture & Benefits

• Competitive salary with an annual discretionary bonus scheme.
• Employee wellbeing benefits, private medical insurance, enhanced pension scheme, 25 days of holiday, and public holidays.
• Flexible remote or hybrid working depending on role and location.
• Ownership, autonomy, career progression, and the opportunity to shape the product and company as it scales.
• Collaborative international culture based on trust, transparency, and ownership.